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Prior Art Search

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RobThePCGuy
prior-art-search

Systematic 7-step methodology for comprehensive patent prior art searches and patentability assessments using BigQuery and CPC classification

Overview

PublisherRobThePCGuy
RepositoryClaude-Patent-Creator
Skill nameprior-art-search
Stars
203
Forks
33
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by RobThePCGuy on GitHub. Read the source before you install it.

Installation

Install the Prior Art Search AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/RobThePCGuy/Claude-Patent-Creator/tree/main/skills/prior-art-search
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/RobThePCGuy/Claude-Patent-Creator.git /tmp/Claude-Patent-Creator
mkdir -p .claude/skills
cp -r /tmp/Claude-Patent-Creator/skills/prior-art-search .claude/skills/prior-art-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prior Art Search in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prior Art Search on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prior Art Search is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Prior Art Search Skill

Systematic 7-step methodology for comprehensive patent prior art searches and patentability assessments.

When to Use

Invoke this skill when users ask to:

  • Conduct prior art search for an invention
  • Assess patentability of an idea
  • Perform freedom-to-operate analysis
  • Find blocking patents
  • Research patent landscapes
  • Prepare for patent filing

What This Skill Does

Implements a professional 7-step prior art search methodology combining:

  • Keyword searches across 100M+ patents (BigQuery)
  • CPC classification searches
  • USPTO API searches
  • Timeline analysis
  • Patentability assessment
  • IDS (Information Disclosure Statement) preparation

The 7-Step Methodology

Step 1: Invention Definition (2-3 min)

Goal: Extract key features and define innovation scope

Process:

  1. Interview user about invention
  2. Extract core technical elements
  3. Identify novel features
  4. List all components/steps
  5. Define search scope

Output: Structured invention summary with key features

Questions to Ask:

  • What problem does this solve?
  • What are the key components/steps?
  • What makes this different from existing solutions?
  • What is the core innovation?

Step 2: Keyword Strategy (2-3 min)

Goal: Develop comprehensive search keyword list

Process:

  1. Primary keywords from invention
  2. Synonyms and variations
  3. Technical terminology
  4. Industry-specific terms
  5. Synonym sets to run as separate searches

Query syntax (important): the keyword search splits a query into terms and requires every term to appear (AND), each matching the title, abstract, or claims (results are ranked by relevance, title hits weighted highest). It does not parse Boolean operators — AND/OR/parentheses are ignored. So:

  • Use plain space-separated terms for the AND set: blockchain authentication.
  • Wrap a phrase in double quotes to require it verbatim: "distributed ledger".
  • For OR / synonyms, run separate searches and merge the results — don't put OR in one query.

Output: Keyword search strategy document

Example:

Primary: blockchain authentication
Synonyms: distributed ledger verification, cryptographic authentication
Technical: public key infrastructure, digital signature
Related: decentralized identity, trustless verification
Searches (run each separately, then merge/dedupe):
- blockchain authentication
- "distributed ledger" verification
- cryptographic authentication
- "public key infrastructure" signature

Step 3: Broad Keyword Search (3-5 min)

Goal: Cast wide net to find relevant patents

Process:

  1. Run keyword searches on BigQuery
  2. Review top 20-30 results per query
  3. Identify most relevant patents
  4. Refine keyword strategy based on results
  5. Document relevant patents found

Code:

python
from mcp_server.bigquery_search import BigQueryPatentSearch
searcher = BigQueryPatentSearch()

results = searcher.search_by_keywords(
    query="blockchain authentication",  # terms AND-matched, ranked by relevance
    limit=30,
    country="US",
    start_year=2015,  # filing year; look back 5-10 years
)

Output: List of 10-20 potentially relevant patents


Step 4: CPC Code Identification (2-3 min)

Goal: Find relevant classification codes

Process:

  1. Extract CPC codes from relevant patents found in Step 3
  2. Analyze CPC code descriptions
  3. Identify primary classification areas
  4. Select 3-5 most relevant CPC codes
  5. Note CPC hierarchies

Common CPC Categories:

  • G06F: Computing/data processing
  • H04L: Digital communication/networks
  • G06Q: Business methods
  • H04W: Wireless communication
  • G06N: AI/neural networks
  • G06T: Image processing

Output: List of relevant CPC codes with descriptions


Step 5: Deep CPC Search (5-10 min)

Goal: Comprehensive search within classifications

Process:

  1. Search each CPC code identified
  2. Review 50-100 patents per CPC code
  3. Read abstracts and claims of top matches
  4. Document closest prior art
  5. Note key differences from invention

Code:

python
results = searcher.search_by_cpc(
    cpc_code="G06F21/",  # Security arrangements
    limit=100,
    country="US"
)

Output: Comprehensive list of potentially blocking patents


Step 6: Timeline Analysis (2-3 min)

Goal: Understand technology evolution

Process:

  1. Filter results by date ranges
  2. Identify filing trends over time
  3. Find recent developments (last 2 years)
  4. Check priority dates
  5. Note technology progression

Code:

python
# Search by year ranges
recent = searcher.search_by_keywords(query, start_year=2022, end_year=2024)
older = searcher.search_by_keywords(query, start_year=2015, end_year=2021)

Output: Timeline showing technology development


Step 7: Patentability Report (5-10 min)

Goal: Professional assessment and recommendations

Process:

  1. Analyze top 10 closest prior art
  2. Assess novelty (35 USC 102)
  3. Assess non-obviousness (35 USC 103)
  4. Rank prior art by relevance
  5. Provide claim strategy recommendations
  6. Generate IDS list

Output: Comprehensive patentability report


Report Format

markdown
# PRIOR ART SEARCH REPORT

## Executive Summary
- Invention: [Brief description]
- Search Date: [Date]
- Searcher: Claude Patent Creator
- Databases: BigQuery (100M+ patents), USPTO API
- Time Period: [Year range]

## Patentability Assessment

### Novelty (35 USC 102)
[Assessment of whether invention is novel]

Score: [High/Medium/Low]

Analysis:
- No exact matches found
- Closest prior art: US10123456
- Key differences: [List]

### Non-Obviousness (35 USC 103)
[Assessment of whether invention is non-obvious]

Score: [High/Medium/Low]

Analysis:
- Combinations considered: [List]
- Motivation to combine: [Analysis]
- Unexpected results: [If any]

## Top 10 Most Relevant Prior Art

### 1. US10123456B2 - [Title] (95% Relevance)
**Assignee**: Example Corp
**Filed**: 2018-03-15
**Granted**: 2019-09-30
**CPC**: G06F21/31, H04L29/06

**Summary**: [Brief abstract]

**Similarities**:
- Uses blockchain for authentication
- Employs public key cryptography
- Distributed verification

**Differences**:
- Does not use [novel feature 1]
- Lacks [novel feature 2]
- Different approach to [aspect]

**Relevance**: High - core technology overlap

---

[Continue for top 10 patents...]

## Search Methodology

### Keywords Used
- Primary: blockchain, authentication, distributed ledger
- Synonyms: cryptographic verification, decentralized identity
- Technical: public key infrastructure, digital signature

### CPC Codes Searched
- G06F21/31 (Authentication)
- H04L29/06 (Security arrangements)
- G06Q20/40 (Payment authentication)

### Databases
- Google BigQuery: 247 results reviewed
- USPTO API: 89 results reviewed
- Total patents analyzed: 336
- Relevant patents identified: 47
- Top prior art selected: 10

## Claim Strategy Recommendations

### Recommended Approach
1. **Focus on novel aspects**: [Specific features]
2. **Claim breadth**: Start broad, add dependent claims
3. **Avoid prior art**: Distinguish from US10123456 by [...]

### Suggested Independent Claim Language

A system for [invention], comprising: [novel element 1]; [novel element 2]; wherein [novel relationship/function]


### Dependent Claim Opportunities
- Specific implementations of [feature]
- Combinations with [technology]
- Variations in [parameter/configuration]

## IDS (Information Disclosure Statement) List

Patents to be disclosed to USPTO:

1. US10123456B2 - [Title]
2. US10234567A1 - [Title]
3. US10345678B1 - [Title]
4. US10456789A1 - [Title]
5. US10567890B2 - [Title]
6. EP3123456A1 - [Title]
7. WO2019/123456 - [Title]
8. US2020/0123456A1 - [Title]
9. US10678901B2 - [Title]
10. US10789012A1 - [Title]

## Conclusion

**Patentability**: [High/Medium/Low]

**Rationale**:
[Summary of why invention is or is not patentable]

**Recommended Next Steps**:
1. [Action item 1]
2. [Action item 2]
3. [Action item 3]

Integration Points

This skill integrates with:

  • BigQuery Patent Search skill (Step 3, 5, 6)
  • MPEP Search skill (For legal guidance)
  • Patent Claims Analyzer (For claim drafting)

Required Data Access

  • Google Cloud BigQuery (100M+ patents)
  • USPTO API (optional, for additional coverage)
  • Internet access for patent retrieval

Estimated Time

  • Quick Search (Steps 1-3): 10-15 minutes
  • Thorough Search (Steps 1-6): 25-35 minutes
  • Complete Report (All 7 steps): 40-60 minutes

Tools Available

  • Bash: To run Python searches
  • Write: To save report and findings
  • Read: To load invention descriptions
  • Grep: To search through results

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prior Art Search AI skill do?

Systematic 7-step methodology for comprehensive patent prior art searches and patentability assessments using BigQuery and CPC classification

Why use Prior Art Search on TypingMind?

Because you install it once and use it with any model. Prior Art Search is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prior Art Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RobThePCGuy/Claude-Patent-Creator/tree/main/skills/prior-art-search. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Prior Art Search?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prior Art Search?

As many as you like. As long as a model supports skills, you can use Prior Art Search with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prior Art Search AI skill free?

Yes. It is published on GitHub by RobThePCGuy under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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